The invention relates to a method for assessing a road type and to an apparatus that is set up for this purpose.
It is well known practice today to use a navigation system to determine the road type that a vehicle is on. To this end, a satellite-based positioning system is used to determine the position of the vehicle and a stored digital map, which also stores the type of the roads, is used to determine the road type. Examples of road types are city roads, country road and freeway.
However, identification of the road type requires the navigation system. Frequently, even in modern vehicles, such a navigation system is still not in place for reasons of cost.
DE 196 06 259 C1 discloses a method for identifying a road type without a digital roadmap being stored in the vehicle. The identification principle is based on there being a certain probability of being able to distinguish particular road types by virtue of characteristic geometric features. The method disclosed therein involves the drive profile being captured geometrically in sections and pattern recognition being used to compare said drive profile with characteristic drive profiles.
The present object for a person skilled in the art is to provide an improved method for assessing a road type and to provide a corresponding apparatus.
This and other objects are achieved by a method, an apparatus, and a motor vehicle according to the invention.
In one aspect, a method for assessing a road type, which method is carried out by an electronic computing device, comprises: reception of environment data based on a sensor measurement; assessment of the road type using a decision tree based on the environment data; assessment of the road type using a state machine based on the environment data; formation of an overall assessment of the road type based on the assessment using the decision tree and the assessment using the state machine.
The use and combination of two different assessment methods allows the accuracy of the assessment of the road type to be improved. In this context, a combination of assessment by means of a decision tree and assessment by means of a state machine has been found to be particularly advantageous. The method allows a decision tree trained using machine learning and a state machine based on human experience while assessing road type to be combined. In this preferred embodiment, the decision tree is induced using inherently known methods of machine learning, based on recorded journey data (training data), which comprise sensor-based environment data, and, as an assessment result for the road type, the road types determined by a navigation system. In other words: the training associates environment data with road types. In this embodiment, the state machine comprises transition rules between states, which represent road types and possibly the state “assessment of road type not possible”, said transition rules being defined by human observation. This structure of the assessment is used to achieve particularly good assessment results. In one setup, freeways were identified with an accuracy of over 90%, for example.
Since the road type identification is not dependent on the data from a navigation system, it is not necessary for the navigation system and the stored digital maps thereof to be updated. Updating the maps can give rise to cost and administrative complexity, which can be avoided by the present invention. The runtime requirements for carrying out the method are low in comparison with the requirements for determining the road type on the basis of a navigation system with a stored digital map.
Environment data may particularly be direct measured values from sensors, or conditioned measured values, such as identified objects in a camera image or radar scan. In general, environment data describe properties of the environment of the vehicle. Suitable sensors are all of the sensors of the vehicle, for example camera systems, 3D camera systems, lateral cameras, reverse cameras, ultrasonic and lidar sensors, speedometers, yaw rate sensors or steering angle sensors. An example of environment data are conditioned data from a camera image that indicate that the lane width is 2.5 m and that the roadway is a directional roadway.
Environment data may be data from a plurality of sensors that are available as a package, for example in a vector. An environment data vector may include the lane width identified in a camera image, the number of signs identified by the camera image, oncoming traffic identified using a camera image or radar scan, identified speed limits, a streetlamp identified using a camera arranged on the side of the vehicle, obstacles identified using ultrasonic sensors or the distance from the vehicle in front, a speed bump identified using lidar sensors and/or the height of the chassis identified using capacitive sensors, for example.
The state machine can be based on human experience while assessing road type. This experience can be retained in all or some transition rules between the states.
In a state machine, the states represent the road types (for example, city road, country road and freeway) and the state that assessment of the road type is not possible or, synonymously, that it is not possible to determine the road type. The transition rules are based on Boolean expressions, which logically combine input data, that is to say environment data, with one another. An example of a transition rule is (road width >2.3 m) && (direction of roadway==Yes)→“freeway”.
The overall assessment formed can be output for a particular trip section for the vehicle. In addition, provision may be made for the output of the assessment to be shaped or maintained on the basis of the particular road type. Thus, there may be provision that if the road type “city” has been assessed, this output is maintained for a trip section of 500 m for the vehicle. If the road type “country road” has been assessed, this type can be maintained for 1000 m. If the road type “freeway” has been assessed, this output can be maintained for 2000 m. When this holding section has passed, a fresh overall assessment can be output. During the holding section, provision may be made for a fresh overall assessment to be performed already, which is then output when the holding section has passed.
In one further development, the formation of an overall assessment includes the setting of the overall assessment to the assessment using the state machine when the assessment using the state machine does not output that a road type cannot be determined; the formation of an overall assessment additionally includes the setting of the overall assessment to the assessment using the decision tree when the assessment using the state machine outputs that a road type cannot be determined. This is particularly advantageous in the case of a decision tree induced by machine learning and a state machine based on human experience. While the road type can be determined using the state machine and hence using rules based on human experience, this assessment of the road type is accorded preference.
In cases in which the state machine cannot provide a statement, however, the decision tree induced by machine learning is drawn upon in order to be able to provide a statement. Assistance systems today and in the future can improve or optimize their operation on the basis of the road type and are reliant on continuous provision of this assessment. The operation of these systems is ensured thereby. Furthermore, it is possible for particular functions of an assistance system or entire assistance systems to be activated only when the vehicle is on a particular road type. At the same time, the use of the same assistance system in different vehicles is made possible, regardless of whether or not the vehicle has a navigation system and hence constant provision of the road type. This avoids matching the assistance system to vehicles with or without a navigation system, which gives rise to costs. In addition, the method utilizes sensors and resources that are typically already in place, or to the evaluations thereof, which does not require any additional components. This in turn saves costs.
The method according to the invention also allows the cable complexity, the physical volume and the programming complexity to be reduced. In addition, a vehicle configuration becomes more flexible, since driver assistance functions that require assessment or determination of the road type do not require a navigation system to be added. Both the decision tree and the state machine can be chosen such that the number of environment data that are needed for the assessment is minimized. This allows computation complexity to be decreased. The decrease in the number of environment data can likewise lead to savings for the supply of power to the sensor systems, since unneeded sensors and the electronic computing systems thereof can be disconnected. Ordinarily, these sensor systems operate independently of one another and disconnection of a sensor system reduces power consumption accordingly.
An assistance system may be an adaptive cruise control (ACC), a high beam assistant, a parking aid, a heat manager, a gearbox manager or a power manager.
In one development, the allocation of a qualification value to the overall assessment is based on whether the overall assessment is based on the assessment using the state machine or the decision tree. The qualification value is used to allow consumers of the overall assessment (for example assistance systems) to rate the reliability of the overall assessment. Thus, an overall assessment that is based on both the assessment using the state machine and the assessment using the decision tree having led to the same result can be allocated a better (possibly higher) qualification value than if the assessment using the decision tree does not confirm the assessment using the state machine. An overall assessment that is based only on assessment using the decision tree can be allocated a poorer (possibly lower) qualification value than if the overall assessment is at least also based on an assessment using the state machine.
In a further development, the method includes the reception of a set of environment data based on sensor measurements, wherein each element of the set has an assigned instant; for each element of the set: creation of a partial assessment of the road type using the decision tree based on the respective element of the set; wherein the allocation of the qualification value is also based on the number of partial assessments that match the overall assessment. This can mean, in particular, that each received element is respectively based on (at least) a sensor measurement that has been carried out at another instant. In this way, a relatively large area of the environment of the vehicle is captured and, by virtue of being split into scans, is used for partial assessment using the decision tree. The qualification value can be determined such that each partial assessment with the same result as the result of the assessment using the state machine increases the qualification value. Partial assessments with a different result, do not, however.
In another further development, the method includes the reception of a set of environment data based on sensor measurements, wherein each element of the set has an assigned instant; wherein the assessment using the decision tree comprises, for each element of the set, creation of a partial assessment of the road type using the decision tree on the basis of the respective element of the set. Assessment of the road type is based on the created partial assessment, particularly using a majority decision.
A set of environment data includes at least two separate environment data. In the case of environment data vectors, a set of environment data has at least two environment vectors. An element of the set is a single vector. Typically, each element is based on sensor data that have been measured at one instant.
The assessment using the decision tree is therefore based on a plurality of partial assessments using the decision tree, with environment data that have a different instant associated with them being used in each case. This can mean, in particular, that each received element is respectively based on (at least) a sensor measurement that has been carried out at a different instant or at a different position of the vehicle. In this way, a relatively large area of the environment of the vehicle is captured and taken into account for the assessment using the decision tree. The reliability of the assessment using the decision tree is thus increased.
In one further development, the method includes the determination of a function parameter of a sensor on the basis of the overall assessment of the road type, particularly the intensity of the power supply or the deactivation of the power supply. In this way, the operation of the sensors can be matched to the situation of the road type, which firstly can increase the performance of the sensors and at the same time, secondly, can save power. In situations in which sensor inputs are not required, the sensor can thus be put into a mode with relatively low power consumption. By way of example, the range of ultrasonic sensors is dependent on the provided level of the power supply. In cases in which obstacles at short distances need to be detected (when parking downtown, for example), the ultrasonic sensors can therefore operate with a smaller supply of current than in cases in which obstacles at greater distances need to be detected (for example, when detecting other road users on the freeway).
In another aspect, an apparatus having an electronic computing unit and a reception unit for environment data is set up to carry out one of the methods discussed above. This apparatus may be a microprocessor, a general-purpose computer or dedicated circuits. The apparatus may be set up to execute program code to carry out the method. The reception unit for environment data may be a standard interface such as USB, CAN, Ethernet, WiFi or Firewire.
Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of one or more preferred embodiments when considered in conjunction with the accompanying drawings.
The same reference symbols relate to corresponding elements throughout the figures.